{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-means Clustering \n",
    "\n",
    "In this this exercise, you will implement the K-means algorithm and use it for image compression. \n",
    "\n",
    "* You will start with a sample dataset that will help you gain an intuition of how the K-means algorithm works. \n",
    "* After that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Implementing K-means](#1)\n",
    "  - [ 1.1 Finding closest centroids](#1.1)\n",
    "    - [ Exercise 1](#ex01)\n",
    "  - [ 1.2 Computing centroid means](#1.2)\n",
    "    - [ Exercise 2](#ex02)\n",
    "- [ 2 - K-means on a sample dataset ](#2)\n",
    "- [ 3 - Random initialization](#3)\n",
    "- [ 4 - Image compression with K-means](#4)\n",
    "  - [ 4.1 Dataset](#4.1)\n",
    "  - [ 4.2 K-Means on image pixels](#4.2)\n",
    "  - [ 4.3 Compress the image](#4.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Implementing K-means\n",
    "\n",
    "The K-means algorithm is a method to automatically cluster similar\n",
    "data points together. \n",
    "\n",
    "* Concretely, you are given a training set $\\{x^{(1)}, ..., x^{(m)}\\}$, and you want\n",
    "to group the data into a few cohesive “clusters”. \n",
    "\n",
    "\n",
    "* K-means is an iterative procedure that\n",
    "     * Starts by guessing the initial centroids, and then \n",
    "     * Refines this guess by \n",
    "         * Repeatedly assigning examples to their closest centroids, and then \n",
    "         * Recomputing the centroids based on the assignments.\n",
    "         \n",
    "\n",
    "* In pseudocode, the K-means algorithm is as follows:\n",
    "\n",
    "    ``` python\n",
    "    # Initialize centroids\n",
    "    # K is the number of clusters\n",
    "    centroids = kMeans_init_centroids(X, K)\n",
    "    \n",
    "    for iter in range(iterations):\n",
    "        # Cluster assignment step: \n",
    "        # Assign each data point to the closest centroid. \n",
    "        # idx[i] corresponds to the index of the centroid \n",
    "        # assigned to example i\n",
    "        idx = find_closest_centroids(X, centroids)\n",
    "\n",
    "        # Move centroid step: \n",
    "        # Compute means based on centroid assignments\n",
    "        centroids = compute_means(X, idx, K)\n",
    "    ```\n",
    "\n",
    "\n",
    "* The inner-loop of the algorithm repeatedly carries out two steps: \n",
    "    * (i) Assigning each training example $x^{(i)}$ to its closest centroid, and\n",
    "    * (ii) Recomputing the mean of each centroid using the points assigned to it. \n",
    "    \n",
    "    \n",
    "* The $K$-means algorithm will always converge to some final set of means for the centroids. \n",
    "\n",
    "* However, that the converged solution may not always be ideal and depends on the initial setting of the centroids.\n",
    "    * Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. \n",
    "    * One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion).\n",
    "\n",
    "You will implement the two phases of the K-means algorithm separately\n",
    "in the next sections. \n",
    "* You will start by completing `find_closest_centroid` and then proceed to complete `compute_centroids`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1.1\"></a>\n",
    "### 1.1 Finding closest centroids\n",
    "\n",
    "In the “cluster assignment” phase of the K-means algorithm, the\n",
    "algorithm assigns every training example $x^{(i)}$ to its closest\n",
    "centroid, given the current positions of centroids. \n",
    "\n",
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "Your task is to complete the code in `find_closest_centroids`. \n",
    "* This function takes the data matrix `X` and the locations of all\n",
    "centroids inside `centroids` \n",
    "* It should output a one-dimensional array `idx` (which has the same number of elements as `X`) that holds the index  of the closest centroid (a value in $\\{1,...,K\\}$, where $K$ is total number of centroids) to every training example .\n",
    "* Specifically, for every example $x^{(i)}$ we set\n",
    "$$c^{(i)} := j \\quad \\mathrm{that \\; minimizes} \\quad ||x^{(i)} - \\mu_j||^2,$$\n",
    "where \n",
    " * $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$ (corresponds to `idx[i]` in the starter code), and \n",
    " * $\\mu_j$ is the position (value) of the $j$’th centroid. (stored in `centroids` in the starter code)\n",
    " \n",
    "If you get stuck, you can check out the hints presented after the cell below to help you with the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UNQ_C1\n",
    "# GRADED FUNCTION: find_closest_centroids\n",
    "\n",
    "def find_closest_centroids(X, centroids):\n",
    "    \"\"\"\n",
    "    Computes the centroid memberships for every example\n",
    "    \n",
    "    Args:\n",
    "        X (ndarray): (m, n) Input values      \n",
    "        centroids (ndarray): k centroids\n",
    "    \n",
    "    Returns:\n",
    "        idx (array_like): (m,) closest centroids\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    # Set K\n",
    "    K = centroids.shape[0]\n",
    "\n",
    "    # You need to return the following variables correctly\n",
    "    idx = np.zeros(X.shape[0], dtype=int)\n",
    "\n",
    "    ### START CODE HERE ###\n",
    "    for i in range(X.shape[0]):\n",
    "          # Array to hold distance between X[i] and each centroids[j]\n",
    "          distance = [] \n",
    "          for j in range(centroids.shape[0]):\n",
    "              norm_ij = np.linalg.norm(X[i] - centroids[j])\n",
    "              distance.append(norm_ij)\n",
    "\n",
    "          idx[i] = np.argmin(distance)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    \n",
    "    \n",
    "* Here's how you can structure the overall implementation for this function\n",
    "    ```python \n",
    "    def find_closest_centroids(X, centroids):\n",
    "    \n",
    "        # Set K\n",
    "        K = centroids.shape[0]\n",
    "    \n",
    "        # You need to return the following variables correctly\n",
    "        idx = np.zeros(X.shape[0], dtype=int)\n",
    "    \n",
    "        ### START CODE HERE ###\n",
    "        for i in range(X.shape[0]):\n",
    "            # Array to hold distance between X[i] and each centroids[j]\n",
    "            distance = [] \n",
    "            for j in range(centroids.shape[0]):\n",
    "                norm_ij = # Your code to calculate the norm between (X[i] - centroids[j])\n",
    "                distance.append(norm_ij)\n",
    "            \n",
    "            idx[i] = # Your code here to calculate index of minimum value in distance\n",
    "        ### END CODE HERE ###\n",
    "        return idx\n",
    "    ```\n",
    "  \n",
    "    If you're still stuck, you can check the hints presented below to figure out how to calculate `norm_ij` and `idx[i]`.\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate norm_ij</b></font></summary>\n",
    "           &emsp; &emsp; You can use <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html\">np.linalg.norm</a> to calculate the norm \n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate norm_ij</b></font></summary>\n",
    "               &emsp; &emsp; You can compute norm_ij as <code>norm_ij = np.linalg.norm(X[i] - centroids[j]) </code>\n",
    "           </details>\n",
    "    </details>\n",
    "\n",
    "     <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate idx[i]</b></font></summary>\n",
    "          &emsp; &emsp; You can use <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.argmin.html\">np.argmin</a> to find the index of the minimum value\n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate idx[i]</b></font></summary>\n",
    "              &emsp; &emsp; You can compute idx[i] as <code>idx[i] = np.argmin(distance)</code>\n",
    "          </details>\n",
    "    </details>\n",
    "        \n",
    "    </details>\n",
    "\n",
    "</details>\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check your implementation using an example dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load an example dataset that we will be using\n",
    "X = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below prints the first five elements in the variable `X` and the dimensions of the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five elements of X are:\n",
      " [[1.84207953 4.6075716 ]\n",
      " [5.65858312 4.79996405]\n",
      " [6.35257892 3.2908545 ]\n",
      " [2.90401653 4.61220411]\n",
      " [3.23197916 4.93989405]]\n",
      "The shape of X is: (300, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"First five elements of X are:\\n\", X[:5]) \n",
    "print('The shape of X is:', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First three elements in idx are: [0 2 1]\n",
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# Select an initial set of centroids (3 Centroids)\n",
    "initial_centroids = np.array([[3,3], [6,2], [8,5]])\n",
    "\n",
    "# Find closest centroids using initial_centroids\n",
    "idx = find_closest_centroids(X, initial_centroids)\n",
    "\n",
    "# Print closest centroids for the first three elements\n",
    "print(\"First three elements in idx are:\", idx[:3])\n",
    "\n",
    "# UNIT TEST\n",
    "from public_tests import *\n",
    "\n",
    "find_closest_centroids_test(find_closest_centroids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table>\n",
    "  <tr>\n",
    "    <td> <b>First three elements in idx are<b></td>\n",
    "    <td> [0 2 1] </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1.2\"></a>\n",
    "### 1.2 Computing centroid means\n",
    "\n",
    "Given assignments of every point to a centroid, the second phase of the\n",
    "algorithm recomputes, for each centroid, the mean of the points that\n",
    "were assigned to it.\n",
    "\n",
    "\n",
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Please complete the `compute_centroids` below to recompute the value for each centroid\n",
    "\n",
    "* Specifically, for every centroid $\\mu_k$ we set\n",
    "$$\\mu_k = \\frac{1}{|C_k|} \\sum_{i \\in C_k} x^{(i)}$$ \n",
    "\n",
    "    where \n",
    "    * $C_k$ is the set of examples that are assigned to centroid $k$\n",
    "    * $|C_k|$ is the number of examples in the set $C_k$\n",
    "\n",
    "\n",
    "* Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k=2$,\n",
    "then you should update $\\mu_2 = \\frac{1}{2}(x^{(3)}+x^{(5)})$.\n",
    "\n",
    "If you get stuck, you can check out the hints presented after the cell below to help you with the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: compute_centpods\n",
    "\n",
    "def compute_centroids(X, idx, K):\n",
    "    \"\"\"\n",
    "    Returns the new centroids by computing the means of the \n",
    "    data points assigned to each centroid.\n",
    "    \n",
    "    Args:\n",
    "        X (ndarray):   (m, n) Data points\n",
    "        idx (ndarray): (m,) Array containing index of closest centroid for each \n",
    "                       example in X. Concretely, idx[i] contains the index of \n",
    "                       the centroid closest to example i\n",
    "        K (int):       number of centroids\n",
    "    \n",
    "    Returns:\n",
    "        centroids (ndarray): (K, n) New centroids computed\n",
    "    \"\"\"\n",
    "    \n",
    "    # Useful variables\n",
    "    m, n = X.shape\n",
    "    \n",
    "    # You need to return the following variables correctly\n",
    "    centroids = np.zeros((K, n))\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    for k in range(K):   \n",
    "          points = X[idx == k]  \n",
    "          centroids[k] = np.mean(points, axis = 0)\n",
    "\n",
    "    ### END CODE HERE ## \n",
    "    \n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    \n",
    "    \n",
    "* Here's how you can structure the overall implementation for this function\n",
    "    ```python \n",
    "    def compute_centroids(X, idx, K):\n",
    "        # Useful variables\n",
    "        m, n = X.shape\n",
    "    \n",
    "        # You need to return the following variables correctly\n",
    "        centroids = np.zeros((K, n))\n",
    "    \n",
    "        ### START CODE HERE ###\n",
    "        for k in range(K):   \n",
    "            points = # Your code here to get a list of all data points in X assigned to centroid k  \n",
    "            centroids[k] = # Your code here to compute the mean of the points assigned\n",
    "    ### END CODE HERE ## \n",
    "    \n",
    "    return centroids\n",
    "    ```\n",
    "  \n",
    "    If you're still stuck, you can check the hints presented below to figure out how to calculate `points` and `centroids[k]`.\n",
    "    \n",
    "    <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate points</b></font></summary>\n",
    "           &emsp; &emsp; Say we wanted to find all the values in X that were assigned to cluster <code>k=0</code>. That is, the corresponding value in idx for these examples is 0. In Python, we can do it as <code>X[idx == 0]</code>. Similarly, the points assigned to centroid <code>k=1</code> are <code>X[idx == 1]</code>\n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate points</b></font></summary>\n",
    "               &emsp; &emsp; You can compute points as <code>points = X[idx == k] </code>\n",
    "           </details>\n",
    "    </details>\n",
    "\n",
    "     <details>\n",
    "          <summary><font size=\"2\" color=\"darkblue\"><b>Hint to calculate centroids[k]</b></font></summary>\n",
    "          &emsp; &emsp; You can use <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.mean.html\">np.mean</a> to find the mean. Make sure to set the parameter <code>axis=0</code> \n",
    "          <details>\n",
    "              <summary><font size=\"2\" color=\"blue\"><b>&emsp; &emsp; More hints to calculate centroids[k]</b></font></summary>\n",
    "              &emsp; &emsp; You can compute centroids[k] as <code>centroids[k] = np.mean(points, axis = 0)</code>\n",
    "          </details>\n",
    "    </details>\n",
    "        \n",
    "    </details>\n",
    "\n",
    "</details>\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now check your implementation by running the cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The centroids are: [[2.42830111 3.15792418]\n",
      " [5.81350331 2.63365645]\n",
      " [7.11938687 3.6166844 ]]\n",
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "K = 3\n",
    "centroids = compute_centroids(X, idx, K)\n",
    "\n",
    "print(\"The centroids are:\", centroids)\n",
    "\n",
    "# UNIT TEST\n",
    "compute_centroids_test(compute_centroids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "2.42830111 3.15792418\n",
    "\n",
    "5.81350331 2.63365645\n",
    "\n",
    "7.11938687 3.6166844 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - K-means on a sample dataset \n",
    "\n",
    "After you have completed the two functions (`find_closest_centroids`\n",
    "and `compute_centroids`) above, the next step is to run the\n",
    "K-means algorithm on a toy 2D dataset to help you understand how\n",
    "K-means works. \n",
    "* We encourage you to take a look at the function (`run_kMeans`) below to understand how it works. \n",
    "* Notice that the code calls the two functions you implemented in a loop.\n",
    "\n",
    "When you run the code below, it will produce a\n",
    "visualization that steps through the progress of the algorithm at\n",
    "each iteration. \n",
    "* At the end, your figure should look like the one displayed in Figure 1.\n",
    "\n",
    "<img src=\"images/figure 1.png\" width=\"500\" height=\"500\">\n",
    "\n",
    "\n",
    "**Note**: You do not need to implement anything for this part. Simply run the code provided below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You do not need to implement anything for this part\n",
    "\n",
    "def run_kMeans(X, initial_centroids, max_iters=10, plot_progress=False):\n",
    "    \"\"\"\n",
    "    Runs the K-Means algorithm on data matrix X, where each row of X\n",
    "    is a single example\n",
    "    \"\"\"\n",
    "    \n",
    "    # Initialize values\n",
    "    m, n = X.shape\n",
    "    K = initial_centroids.shape[0]\n",
    "    centroids = initial_centroids\n",
    "    previous_centroids = centroids    \n",
    "    idx = np.zeros(m)\n",
    "    \n",
    "    # Run K-Means\n",
    "    for i in range(max_iters):\n",
    "        \n",
    "        #Output progress\n",
    "        print(\"K-Means iteration %d/%d\" % (i, max_iters-1))\n",
    "        \n",
    "        # For each example in X, assign it to the closest centroid\n",
    "        idx = find_closest_centroids(X, centroids)\n",
    "        \n",
    "        # Optionally plot progress\n",
    "        if plot_progress:\n",
    "            plot_progress_kMeans(X, centroids, previous_centroids, idx, K, i)\n",
    "            previous_centroids = centroids\n",
    "            \n",
    "        # Given the memberships, compute new centroids\n",
    "        centroids = compute_centroids(X, idx, K)\n",
    "    plt.show() \n",
    "    return centroids, idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means iteration 0/9\n",
      "K-Means iteration 1/9\n",
      "K-Means iteration 2/9\n",
      "K-Means iteration 3/9\n",
      "K-Means iteration 4/9\n",
      "K-Means iteration 5/9\n",
      "K-Means iteration 6/9\n",
      "K-Means iteration 7/9\n",
      "K-Means iteration 8/9\n",
      "K-Means iteration 9/9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load an example dataset\n",
    "X = load_data()\n",
    "\n",
    "# Set initial centroids\n",
    "initial_centroids = np.array([[3,3],[6,2],[8,5]])\n",
    "K = 3\n",
    "\n",
    "# Number of iterations\n",
    "max_iters = 10\n",
    "\n",
    "centroids, idx = run_kMeans(X, initial_centroids, max_iters, plot_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"3\"></a>\n",
    "## 3 - Random initialization\n",
    "\n",
    "The initial assignments of centroids for the example dataset was designed so that you will see the same figure as in Figure 1. In practice, a good strategy for initializing the centroids is to select random examples from the\n",
    "training set.\n",
    "\n",
    "In this part of the exercise, you should understand how the function `kMeans_init_centroids` is implemented.\n",
    "* The code first randomly shuffles the indices of the examples (using `np.random.permutation()`). \n",
    "* Then, it selects the first $K$ examples based on the random permutation of the indices. \n",
    "    * This allows the examples to be selected at random without the risk of selecting the same example twice.\n",
    "\n",
    "**Note**: You do not need to make implement anything for this part of the exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You do not need to modify this part\n",
    "\n",
    "def kMeans_init_centroids(X, K):\n",
    "    \"\"\"\n",
    "    This function initializes K centroids that are to be \n",
    "    used in K-Means on the dataset X\n",
    "    \n",
    "    Args:\n",
    "        X (ndarray): Data points \n",
    "        K (int):     number of centroids/clusters\n",
    "    \n",
    "    Returns:\n",
    "        centroids (ndarray): Initialized centroids\n",
    "    \"\"\"\n",
    "    \n",
    "    # Randomly reorder the indices of examples\n",
    "    randidx = np.random.permutation(X.shape[0])\n",
    "    \n",
    "    # Take the first K examples as centroids\n",
    "    centroids = X[randidx[:K]]\n",
    "    \n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"4\"></a>\n",
    "## 4 - Image compression with K-means\n",
    "\n",
    "In this exercise, you will apply K-means to image compression. \n",
    "\n",
    "* In a straightforward 24-bit color representation of an image$^{2}$, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often refered to as the RGB encoding.\n",
    "* Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of\n",
    "colors to 16 colors.\n",
    "* By making this reduction, it is possible to represent (compress) the photo in an efficient way. \n",
    "* Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n",
    "\n",
    "In this part, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image.\n",
    "* Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3- dimensional RGB space. \n",
    "* Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\n",
    "\n",
    "<img src=\"images/figure 2.png\" width=\"500\" height=\"500\">\n",
    "\n",
    "$^{2}$<sub>The provided photo used in this exercise belongs to Frank Wouters and is used with his permission.</sub>\n",
    "\n",
    "<a name=\"4.1\"></a>\n",
    "### 4.1 Dataset\n",
    "\n",
    "**Load image**\n",
    "\n",
    "First, you will use `matplotlib` to read in the original image, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load an image of a bird\n",
    "original_img = plt.imread('bird_small.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualize image**\n",
    "\n",
    "You can visualize the image that was just loaded using the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fec91c97650>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the image\n",
    "plt.imshow(original_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the dimension of the variable**\n",
    "\n",
    "As always, you will print out the shape of your variable to get more familiar with the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of original_img is: (128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of original_img is:\", original_img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this creates a three-dimensional matrix `original_img` where \n",
    "* the first two indices identify a pixel position, and\n",
    "* the third index represents red, green, or blue. \n",
    "\n",
    "For example, `original_img[50, 33, 2]` gives the blue intensity of the pixel at row 50 and column 33.\n",
    "\n",
    "#### Processing data\n",
    "\n",
    "To call the `run_kMeans`, you need to first transform the matrix `original_img` into a two-dimensional matrix.\n",
    "\n",
    "* The code below reshapes the matrix `original_img` to create an $m \\times 3$ matrix of pixel colors (where\n",
    "$m=16384 = 128\\times128$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide by 255 so that all values are in the range 0 - 1\n",
    "original_img = original_img / 255\n",
    "\n",
    "# Reshape the image into an m x 3 matrix where m = number of pixels\n",
    "# (in this case m = 128 x 128 = 16384)\n",
    "# Each row will contain the Red, Green and Blue pixel values\n",
    "# This gives us our dataset matrix X_img that we will use K-Means on.\n",
    "\n",
    "X_img = np.reshape(original_img, (original_img.shape[0] * original_img.shape[1], 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"4.2\"></a>\n",
    "### 4.2 K-Means on image pixels\n",
    "\n",
    "Now, run the cell below to run K-Means on the pre-processed image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means iteration 0/9\n",
      "K-Means iteration 1/9\n",
      "K-Means iteration 2/9\n",
      "K-Means iteration 3/9\n",
      "K-Means iteration 4/9\n",
      "K-Means iteration 5/9\n",
      "K-Means iteration 6/9\n",
      "K-Means iteration 7/9\n",
      "K-Means iteration 8/9\n",
      "K-Means iteration 9/9\n"
     ]
    }
   ],
   "source": [
    "# Run your K-Means algorithm on this data\n",
    "# You should try different values of K and max_iters here\n",
    "K = 16                       \n",
    "max_iters = 10               \n",
    "\n",
    "# Using the function you have implemented above. \n",
    "initial_centroids = kMeans_init_centroids(X_img, K) \n",
    "\n",
    "# Run K-Means - this takes a couple of minutes\n",
    "centroids, idx = run_kMeans(X_img, initial_centroids, max_iters) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of idx: (16384,)\n",
      "Closest centroid for the first five elements: [1 2 2 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of idx:\", idx.shape)\n",
    "print(\"Closest centroid for the first five elements:\", idx[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"4.3\"></a>\n",
    "### 4.3 Compress the image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After finding the top $K=16$ colors to represent the image, you can now\n",
    "assign each pixel position to its closest centroid using the\n",
    "`find_closest_centroids` function. \n",
    "* This allows you to represent the original image using the centroid assignments of each pixel. \n",
    "* Notice that you have significantly reduced the number of bits that are required to describe the image. \n",
    "    * The original image required 24 bits for each one of the $128\\times128$ pixel locations, resulting in total size of $128 \\times 128 \\times 24 = 393,216$ bits. \n",
    "    * The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. \n",
    "    * The final number of bits used is therefore $16 \\times 24 + 128 \\times 128 \\times 4 = 65,920$ bits, which corresponds to compressing the original image by about a factor of 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Represent image in terms of indices\n",
    "X_recovered = centroids[idx, :] \n",
    "\n",
    "# Reshape recovered image into proper dimensions\n",
    "X_recovered = np.reshape(X_recovered, original_img.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can view the effects of the compression by reconstructing\n",
    "the image based only on the centroid assignments. \n",
    "* Specifically, you can replace each pixel location with the mean of the centroid assigned to\n",
    "it. \n",
    "* Figure 3 shows the reconstruction we obtained. Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\n",
    "\n",
    "<img src=\"images/figure 3.png\" width=\"700\" height=\"700\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display original image\n",
    "fig, ax = plt.subplots(1,2, figsize=(8,8))\n",
    "plt.axis('off')\n",
    "\n",
    "ax[0].imshow(original_img*255)\n",
    "ax[0].set_title('Original')\n",
    "ax[0].set_axis_off()\n",
    "\n",
    "\n",
    "# Display compressed image\n",
    "ax[1].imshow(X_recovered*255)\n",
    "ax[1].set_title('Compressed with %d colours'%K)\n",
    "ax[1].set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
